Judgment Extremity and Accuracy Under Epistemic vs. Aleatory Uncertainty

People view uncertain events as knowable in principle (epistemic uncertainty), as fundamentally random (aleatory uncertainty), or as some mixture of the two. We show that people make more extreme probability judgments (i.e., closer to 0 or 1) for events they view as entailing more epistemic uncertainty and less aleatory uncertainty. We demonstrate this pattern in a domain where there is agreement concerning the balance of evidence (pairings of teams according to their seed in a basketball tournament) but individual differences in the perception of the epistemicness/aleatoriness of that domain (Study 1), across a range of domains that vary in their perceived epistemicness/aleatoriness (Study 2), in a single judgment task for which we only vary the degree of randomness with which events are selected (Study 3), and when we prime participants to see events as more epistemic or aleatory (Study 4). Decomposition of accuracy scores suggests that the greater judgment extremity of more epistemic events can manifes...

[1]  Leslie E. Papke,et al.  Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates , 1993 .

[2]  Reid Hastie,et al.  Calibration Trumps Confidence as a Basis for Witness Credibility , 2006, Psychological science.

[3]  A. Tversky,et al.  The weighing of evidence and the determinants of confidence , 1992, Cognitive Psychology.

[4]  Lyle Brenner,et al.  A random support model of the calibration of subjective probabilities , 2003 .

[5]  R. Herrnstein,et al.  The Matching Law Papers in Psychology and Economics , 1997 .

[6]  J. Goodnow Determinants of choice-distribution in two-choice situations. , 1955, The American journal of psychology.

[7]  D. Yves von Cramon,et al.  Variants of uncertainty in decision-making and their neural correlates , 2005, Brain Research Bulletin.

[8]  Amos Tversky,et al.  On the evaluation of probability judgments : calibration, resolution, and monotonicity , 1993 .

[9]  A. Tversky,et al.  Heuristics and Biases: Unpacking, Repacking, and Anchoring: Advances in Support Theory , 2002 .

[10]  Craig R. Fox,et al.  Partition Priming in Judgment Under Uncertainty , 2003, Psychological science.

[11]  Ivan A. Iachine,et al.  Robust tests for the equality of variances for clustered data , 2010 .

[12]  Robert T. Clemen,et al.  Subjective Probability Assessment in Decision Analysis: Partition Dependence and Bias Toward the Ignorance Prior , 2005, Manag. Sci..

[13]  Ayleen Wisudha,et al.  Distribution of probability assessments for almanac and future event questions , 1982 .

[14]  I. Erev,et al.  Simultaneous Over- and Underconfidence: The Role of Error in Judgment Processes. , 1994 .

[15]  Peter Ayton,et al.  Task influences on judgemental forecasting , 1987 .

[16]  Yaacov Trope,et al.  Inferences of personal characteristics on the basis of information retrieved from one's memory. , 1978 .

[17]  Kelly E. See,et al.  Between ignorance and truth: Partition dependence and learning in judgment under uncertainty. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[18]  C. Fox Strength of Evidence, Judged Probability, and Choice Under Uncertainty , 1999, Cognitive Psychology.

[19]  Michael J. Olson,et al.  Patterns of preference for numerical and verbal probabilities. , 1997 .

[20]  Derek J. Koehler,et al.  A Strength Model of Probability Judgments for Tournaments , 1996 .

[21]  B. Fischhoff,et al.  I knew it would happen: Remembered probabilities of once—future things , 1975 .

[22]  David Faust,et al.  Eliminating the hindsight bias. , 1988 .

[23]  Jack B. Soll,et al.  Overconfidence: It Depends on How, What, and Whom You Ask. , 1999, Organizational behavior and human decision processes.

[24]  David L. Ronis,et al.  Components of probability judgment accuracy: Individual consistency and effects of subject matter and assessment method. , 1987 .

[25]  A. H. Murphy A New Vector Partition of the Probability Score , 1973 .

[26]  Baruch Fischhoff,et al.  Calibration of Probabilities: The State of the Art , 1977 .

[27]  L. Ross,et al.  The role of construal processes in overconfident predictions about the self and others. , 1990, Journal of personality and social psychology.

[28]  Ian Hacking,et al.  The Emergence of Probability. A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference , 1979 .

[29]  A. A. Probability, Statistics and Truth , 1940, Nature.

[30]  Michael B. Miller,et al.  Searching for patterns in random sequences. , 2004, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[31]  G. Brier,et al.  External correspondence: Decompositions of the mean probability score , 1982 .

[32]  A. Wald,et al.  Probability, statistics and truth , 1939 .

[33]  A. Tversky,et al.  A Belief-Based Account of Decision Under Uncertainty , 1998 .

[34]  Stephen J. Hoch,et al.  Counterfactual reasoning and accuracy in predicting personal events. , 1985 .

[35]  B. Fischhoff,et al.  Reasons for confidence. , 1980 .

[36]  Baruch Fischhoff,et al.  Fifty–fifty = 50%? , 1999 .

[37]  Desmond L. Bell,et al.  The emergence of probability: A philosophical study of early ideas about probability, induction and statistical inference , 1987 .

[38]  L. Ross,et al.  The overconfidence effect in social prediction. , 1990, Journal of personality and social psychology.

[39]  P Killeen,et al.  The matching law. , 1972, Journal of the experimental analysis of behavior.

[40]  Gülden Ülkümen,et al.  Two dimensions of subjective uncertainty: Clues from natural language. , 2016, Journal of experimental psychology. General.

[41]  Daniel C. Krawczyk,et al.  (www.interscience.wiley.com) DOI: 10.1002/bdm.575 The Transience of Constructed Preferences , 2008 .

[42]  J. Schreiber Foundations Of Statistics , 2016 .

[43]  Leif D. Nelson,et al.  False-Positive Psychology , 2011, Psychological science.

[44]  George Wright,et al.  Changes in the realism and distribution of probability assessments as a function of question type , 1982 .

[45]  S. Lichtenstein,et al.  Do those who know more also know more about how much they know?*1 , 1977 .

[46]  William C. Howell,et al.  A test of task influences in uncertainty measurement , 1982 .

[47]  E. Robinson,et al.  Children's sensitivity to their own relative ignorance: handling of possibilities under epistemic and physical uncertainty. , 2006, Child development.

[48]  G. Brier VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[49]  D. Moore,et al.  The trouble with overconfidence. , 2008, Psychological review.

[50]  D. Yves von Cramon,et al.  Why am I unsure? Internal and external attributions of uncertainty dissociated by fMRI , 2004, NeuroImage.

[51]  B. Carlson,et al.  The Accuracy of Future Forecasts and Past Judgments , 1993 .

[52]  B. Fischhoff,et al.  Calibration of probabilities: the state of the art to 1980 , 1982 .

[53]  PAUL M. FITTS,et al.  S-R COMPATIBILITY : SPATIAL CHARACTERISTICS STIMULUS AND RESPONSE , 2004 .

[54]  H. Levene Robust tests for equality of variances , 1961 .

[55]  G. Keren Calibration and probability judgements: Conceptual and methodological issues , 1991 .

[56]  D. W. Hands The Matching Law: Papers In Psychology And Economics , 1999 .

[57]  Michael Smithson,et al.  A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. , 2006, Psychological methods.

[58]  GUlden Ulkiimen,et al.  DISTINGUISHING TWO DIMENSIONS OF UNCERTAINTY , 2011 .

[59]  W. Gaissmaier,et al.  The smart potential behind probability matching , 2008, Cognition.

[60]  P. Fitts,et al.  S-R compatibility: spatial characteristics of stimulus and response codes. , 1953, Journal of experimental psychology.

[61]  Derek J. Koehler,et al.  Modeling patterns of probability calibration with random support theory: Diagnosing case-based judgment ☆ , 2005 .

[62]  J. Unturbe,et al.  Probability matching involves rule-generating ability: a neuropsychological mechanism dealing with probabilities. , 2007, Neuropsychology.

[63]  A. Tversky,et al.  Support theory: A nonextensional representation of subjective probability. , 1994 .

[64]  C. Wickens Engineering psychology and human performance, 2nd ed. , 1992 .

[65]  Jack B. Soll Determinants of Overconfidence and Miscalibration: The Roles of Random Error and Ecological Structure☆ , 1996 .

[66]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .